SoroushVahidi/scidocs huggingface dataset:
SPIKING NEURAL NETWORKS SPIKING NEURAL NETWORKS
(2003)

Background: Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth. Methods/design: PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated. Conclusion: The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis. Published: 29 September 2004 BMC Surgery 2004, 4:12 doi:10.1186/1471-2482-4-12 Received: 21 July 2004 Accepted: 29 September 2004 This article is available from: http://www.biomedcentral.com/1471-2482/4/12 © 2004 Besselink et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Randoop: feedback-directed random testing for Java
(2007)

R<scp>ANDOOP</scp> for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R<scp>ANDOOP</scp>'s input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R<scp>ANDOOP</scp>'s behavior and output.
An orchestrated survey of methodologies for automated software test case generation
(2013)

Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is odel-based testing contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a rchestrated survey earch-based software testing
Pex-White Box Test Generation for .NET
(2008)

Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to pathbounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.
Software Testing Research: Achievements, Challenges, Dreams
(2007)

Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Testing, the subject of this paper, is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. Indeed, software testing is a broad term encompassing a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of the most relevant challenges to be addressed is here proposed. In it, the starting point is constituted by some important past achievements, while the destination consists of four identified goals to which research ultimately tends, but which remain as unreachable as dreams. The routes from the achievements to the dreams are paved by the outstanding research challenges, which are discussed in the paper along with interesting ongoing work.
SEGAN: Speech Enhancement Generative Adversarial Network
(2017)

Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
Opposite Effects of Δ-9-Tetrahydrocannabinol and Cannabidiol on Human Brain Function and Psychopathology
(2010)

Δ-9-tetrahydrocannabinol (Δ-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Δ-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Δ-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Δ-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Δ-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Δ-9-THC. Δ-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Δ-9-tetrahydrocannabinol. Δ-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD's ability to block the psychotogenic effects of Δ-9-THC.
Graph Based Relational Features for Collective Classification
(2015)

Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.
Key organizational factors in data warehouse architecture selection
(2010)

a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates what is variously called the hub and spoke architecture (i.e., a centralized data warehouse with dependent data marts), Corporate Information Factory, DW 2.0 (i.e., Inmon's current term), or enterprise data warehouse architecture [23]. Another architecture alternative is the data mart bus architecture with linked dimensional data marts (i.e., bus architecture), advocated by Ralph Kimball, the other preeminent figure in data warehousing [52]. 1 These and other architectures (e.g., independent data marts and federated) have fundamental differences and strong advocates. The selection of an appropriate architecture is an important decision. A study by the Meta Group found that architecture selection is one of the key factors influencing data warehousing success [54]. A Gartner report identified the architecture selection decision as one of the five problem …
Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks
(2016)

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, an experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called Experienced Grey Wolf Optimizer (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various datasets demonstrate an advance of the EGWO over the original GWO and other meta-heuristics such as genetic algorithms and particle swarm optimization.
Second-order complex random vectors and normal distributions
(1996)

We formulate as a deconvolution problem the causalhoncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential aljporithm presented in a recent paper by Shalvi and Weinstein is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based b u t does not present the high comlputational complexity of methods proposed up to now.
One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications
(2011)

Lack of a suitable selection device to suppress sneak current has impeded the development of 4F2 crossbar memory array utilizing stable and scalable bipolar resistive-switching. We report a high-performance nonlinear bipolar selector realized by a simple Ni/TiO2/Ni MIM structure with a high current density of 105 A/cm2, and a Ni/TiO2/Ni/HfO2/Pt vertically stacked 1S1R cell capable of gigabit memory implementation. Furthermore, the demonstration of 1S1R array fabricated completely at room temperature on a plastic substrate highlights the promise of future extremely low-cost flexible nonvolatile memory.
A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery
(2016)

Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.
Methods for ordinal peer grading
(2014)

Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.
Watch Me Code: Programming Mentorship Communities on Twitch.tv
(2018)

Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.
A multi-dimensional meter-adaptive method for automatic segmentation of music
(2015)

Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.
X-shaped inferior extensor retinaculum and its doubtful use in the Bröstrom–Gould procedure
(2017)

The inferior extensor retinaculum (IER) is an aponeurotic structure located in the anterior aspect of the ankle. According to the literature, it can be used to reinforce a repair of the anterior talofibular ligament in ankle instability. Despite its usual description as an Y-shaped structure, it is still unclear which part of the retinaculum is used for this purpose, or if it is instead the crural fascia that is being used. The purpose of this study is to define the anatomical characteristics of the IER to better understand its role in the Broström–Gould procedure. Twenty-one ankles were dissected. The morphology of the IER and its relationship with neighbouring structures were recorded. Seventeen (81%) of the IER in this study had an X-shaped morphology, with the presence of an additional oblique superolateral band. This band, by far the thinnest of the retinaculum, is supposed to be used to reinforce the repair of the anterior talofibular ligament. The intermediate dorsal cutaneous nerve (lateral branch of the superficial peroneal nerve) was found to cross the retinaculum in all cases. The IER is most commonly seen as an X-shaped structure, but the fact that the oblique superolateral band is a thin band of tissue probably indicates that it may not add significant strength to ankle stability. Furthermore, the close relationship of the retinaculum with the superficial peroneal nerve is another factor to consider before deciding to perform a Broström–Gould procedure. These anatomical findings advise against the use of the Gould augmentation.
Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies
(2014)

Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.